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Related Concept Videos

Heart Failure IV: Classification and Diagnostic Evaluation01:30

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Heart failure can be classified in various ways, with the most common classifications based on physical activity limitations, disease progression, severity, and treatment strategies.The Functional Classification of Heart Failure divides patients into four categories based on physical activity limitation due to symptom burden.Class I: Patients in this class have cardiac disease but no physical activity limitations. Ordinary activities like walking, climbing stairs, or routine tasks do not cause...
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Heart Failure VII: Nursing Interventions01:30

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The first step in nursing management of a patient with heart failure involves thoroughly assessing the patient's medical history.Subjective Data: Obtain the patient's medical history of coronary artery disease, hypertension, myocardial infarction, and symptoms like dyspnea, orthopnea, and paroxysmal nocturnal dyspnea.Objective Data: Conduct a physical examination to identify findings such as jugular vein distention, pulmonary crackles, tachycardia, murmurs, peripheral edema, and vital signs,...
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Heart Failure VI: Adjunct Therapies01:22

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Additional therapies for treating patients with heart failure (HF) may include procedural interventions, supplemental oxygen, the management of sleep disorders, and nutritional therapy.Procedural InterventionsImplantable Cardioverter-Defibrillator: For patients at risk of life-threatening arrhythmias due to severe left ventricular dysfunction, an Implantable Cardioverter-Defibrillator (ICD) can detect and terminate these arrhythmias, preventing sudden cardiac death and improving survival rates.
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Heart Failure V: Medical Management01:30

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Medical Management of Acute Decompensated Heart Failure (ADHF)The primary goals of therapy for patients hospitalized with acute decompensated heart failure (ADHF) include:Relieving symptomsOptimizing volume statusSupporting oxygenation and ventilationMaintaining cardiac output (CO) and end-organ perfusionIdentifying and addressing the cause of ADHFPreventing complicationsProviding patient education on factors precipitating HF exacerbationPlanning for dischargeOngoing monitoring and assessment...
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The activation of the sympathetic nervous system and the renin-angiotensin-aldosterone system (RAAS) contributes to cardiac remodeling, and inhibiting the RAAS is a pharmacological target in heart failure management. As a result, neurohumoral modulation is a crucial treatment principle for managing heart failure. This approach involves using medications like ACE inhibitors (ACEIs), angiotensin receptor blockers (ARBs), β-blockers, mineralocorticoid receptor antagonists (MRAs), and neutral...
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Assessment: Nursing management of patients with cardiomyopathy begins with a thorough assessment of the patient's history, including a family history of cardiomyopathy or sudden cardiac death, personal history of heart disease, hypertension, diabetes, and any alcohol consumption or drug use.During the physical examination, assess vital signs, look for signs of heart failure (such as edema, jugular venous distention, and cyanosis), auscultate for abnormal heart sounds (like murmurs and gallops),...
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Predicting adherence of patients with HF through machine learning techniques.

Georgia Spiridon Karanasiou1, Evanthia Eleftherios Tripoliti1, Theofilos Grigorios Papadopoulos2

  • 1Department of Biomedical Research , Institute of Molecular Biology and Biotechnology , FORTH, GR 45110 Ioannina , Greece.

Healthcare Technology Letters
|October 14, 2016
PubMed
Summary

Machine learning models can predict heart failure (HF) patient adherence to treatment, including medication, nutrition, and physical activity. Accurate prediction aids in personalized patient management and improves outcomes for this chronic disease.

Keywords:
cardiologychronic diseasediseasesheart failurelearning (artificial intelligence)machine learning techniquesmedicationnutritionpatient adherence predictionpatient monitoringpatient treatmentphysical activity

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Area of Science:

  • Cardiology
  • Medical Informatics
  • Machine Learning

Background:

  • Heart failure (HF) significantly impacts patient quality of life, leading to frequent hospitalizations and high mortality.
  • Patient adherence to prescribed treatments is crucial for mitigating severe HF consequences.
  • High rates of non-adherence necessitate predictive tools for proactive patient management.

Purpose of the Study:

  • To develop and evaluate machine learning models for predicting patient adherence in heart failure.
  • To classify patients based on medication adherence and global adherence (medication, nutrition, physical activity).

Main Methods:

  • Employed 11 classification algorithms combined with feature selection and resampling techniques.
  • Evaluated models on a dataset of 90 heart failure patients.
  • Patient adherence was determined by clinician estimation.

Main Results:

  • Achieved a highest detection accuracy of 82% for predicting global adherence.
  • Achieved a highest detection accuracy of 91% for predicting medication adherence.
  • Demonstrated the potential of machine learning in identifying adherence patterns.

Conclusions:

  • Machine learning models show high accuracy in predicting heart failure patient adherence.
  • Predictive models can assist clinicians in tailoring patient monitoring and management strategies.
  • Improved adherence prediction can lead to better patient outcomes and reduced healthcare burden.